Developing an Enhanced IHBO Algorithm with Chaotic Binary Hashing to Optimize BiGRU for Industry 4.0 IIoT Anomaly Detection
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T. Vaiyapuri, Z. Sbai, H. Alaskar, and N. A. Alaseem, "Deep learning approaches for intrusion detection in IIoT networks–opportunities and future directions," International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, 2021.
M. Abdel-Basset, V. Chang, H. Hawash, R. K. Chakrabortty, and M. Ryan, "Deep-IFS: Intrusion detection approach for industrial internet of things traffic in fog environment," IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7704-7715, 2020.
M. Mohy-Eddine, A. Guezzaz, S. Benkirane, and M. Azrour, "An effective intrusion detection approach based on ensemble learning for IIoT edge computing," Journal of Computer Virology and Hacking Techniques, vol. 19, no. 4, pp. 469-481, 2023.
M. Nuaimi, L. C. Fourati, and B. B. Hamed, "Intelligent approaches toward intrusion detection systems for Industrial Internet of Things: A systematic comprehensive review," Journal of Network and Computer Applications, vol. 215, p. 103637, 2023.
S. Alem, D. Espes, L. Nana, E. Martin, and F. De Lamotte, "A novel bi-anomaly-based intrusion detection system approach for industry 4.0," Future Generation Computer Systems, vol. 145, pp. 267-283, 2023.
X. Li, C. Xie, Z. Zhao, C. Wang, and H. Yu, "Anomaly Detection Algorithm of Industrial Internet of Things Data Platform Based on Deep Learning," IEEE Transactions on Green Communications and Networking, 2024.
N. Tuptuk and S. Hailes, "Security of smart manufacturing systems," Journal of Manufacturing Systems, vol. 47, 05/21 2018, doi: 10.1016/j.jmsy.2018.04.007.
R. Abdulhammed, H. Musafer, A. Alessa, M. Faezipour, and A. Abuzneid, "Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection," Electronics, vol. 8, no. 3, p. 322, 2019. [Online]. Available: https://www.mdpi.com/2079-9292/8/3/322.
H. Liu and B. Lang, "Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey," Applied Sciences, vol. 9, no. 20, p. 4396, 2019. [Online]. Available: https://www.mdpi.com/2076-3417/9/20/4396.
Y. Yu, X. Si, C. Hu, and J. Zhang, "A review of recurrent neural networks: LSTM cells and network architectures," Neural computation, vol. 31, no. 7, pp. 1235-1270, 2019.
G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Computers & Electrical Engineering, vol. 40, no. 1, pp. 16-28, 2014.
F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation, vol. 192, pp. 84-110, 2022.
B. Lahasan and H. Samma, "Optimized deep autoencoder model for Internet of Things intruder detection," IEEE Access, vol. 10, pp. 8434-8448, 2022.
Y. Zhang and Q. Liu, "On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples," Future Generation Computer Systems, vol. 133, pp. 213-227, 2022/08/01/ 2022, doi: https://doi.org/10.1016/j.future.2022.03.007.
J. A. Faysal et al., "XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection," Telecom, vol. 3, no. 1, pp. 52-69, 2022. [Online]. Available: https://www.mdpi.com/2673-4001/3/1/3.
B. S. Akash, P. K. R. Yannam, B. V. S. Ruthvik, L. Kumar, L. B. Murthy, and A. Krishna, "Predicting Cyber-Attacks on IoT Networks Using Deep-Learning and Different Variants of SMOTE," Cham, 2022: Springer International Publishing, in Advanced Information Networking and Applications, pp. 243-255.
F. S. Alrayes et al., "Modeling of Botnet Detection Using Barnacles Mating Optimizer with Machine Learning Model for Internet of Things Environment," Electronics, vol. 11, no. 20, p. 3411, 2022. [Online]. Available: https://www.mdpi.com/2079-9292/11/20/3411.
S. M. Kasongo, "A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework," Computer Communications, vol. 199, pp. 113-125, 2023.
H. Nandanwar and R. Katarya, "Deep learning enabled intrusion detection system for Industrial IOT environment," Expert Systems with Applications, vol. 249, p. 123808, 2024.
A. V. Turukmane and R. Devendiran, "M-MultiSVM: An efficient feature selection assisted network intrusion detection system using machine learning," Computers & Security, vol. 137, p. 103587, 2024.
M. H. Nadimi-Shahraki, H. Zamani, Z. Asghari Varzaneh, and S. Mirjalili, "A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations," Archives of Computational Methods in Engineering, vol. 30, no. 7, pp. 4113-4159, 2023.
N. Moustafa and J. Slay, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," in Proc. 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, pp. 1–6, 2015.